Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance.

Advances in spatial proteomics and protein colocalization are a driving force in the understanding of cellular mechanisms and their influence on biological processes. New methods in the field of spatial proteomics call for the development of algorithms and open up new avenues of research. The newly introduced Molecular Pixelation (MPX) provides spatial information on surface proteins and their relationship with each other in single cells. This allows for in silico representation of neighborhoods of membrane proteins as graphs. In order to analyze this new data modality, we adapted local assortativity in networks of MPX single-cell graphs and created a method that is able to capture detailed information on the spatial relationships of proteins. The introduced method can evaluate the pairwise colocalization of proteins and access higher-order similarity to investigate the colocalization of multiple proteins at the same time. We evaluated the method using publicly available MPX datasets where T cells were treated with a chemokine to study uropod formation. We demonstrate that adjusted local assortativity detects the effects of the stimuli at both single- and multiple-marker levels, which enhances our understanding of the uropod formation. We also applied our method to treating cancerous B-cell lines using a therapeutic antibody. With the adjusted local assortativity, we recapitulated the effect of rituximab on the polarity of CD20. Our computational method together with MPX improves our understanding of not only the formation of cell polarity and protein colocalization under stimuli but also advancing the overall insight into immune reaction and reorganization of cell surface proteins, which in turn allows the design of novel therapies. We foresee its applicability to other types of biological spatial data when represented as undirected graphs.

Transcriptome sequencing in pediatric acute lymphoblastic leukemia identifies fusion genes associated with distinct DNA methylation profiles.

BACKGROUND: Structural chromosomal rearrangements that lead to expressed fusion genes are a hallmark of acute lymphoblastic leukemia (ALL). In this study, we performed transcriptome sequencing of 134 primary ALL patient samples to comprehensively detect fusion transcripts. METHODS: We combined fusion gene detection with genome-wide DNA methylation analysis, gene expression profiling, and targeted sequencing to determine molecular signatures of emerging ALL subtypes. RESULTS: We identified 64 unique fusion events distributed among 80 individual patients, of which over 50% have not previously been reported in ALL. Although the majority of the fusion genes were found only in a single patient, we identified several recurrent fusion gene families defined by promiscuous fusion gene partners, such as ETV6, RUNX1, PAX5, and ZNF384, or recurrent fusion genes, such as DUX4-IGH. Our data show that patients harboring these fusion genes displayed characteristic genome-wide DNA methylation and gene expression signatures in addition to distinct patterns in single nucleotide variants and recurrent copy number alterations. CONCLUSION: Our study delineates the fusion gene landscape in pediatric ALL, including both known and novel fusion genes, and highlights fusion gene families with shared molecular etiologies, which may provide additional information for prognosis and therapeutic options in the future.

DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia.

BACKGROUND: We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL. RESULTS: We used the methylation status of ~450,000 CpG sites in 546 well-characterized patients with T-ALL or seven recurrent B-cell precursor ALL subtypes to design and validate sensitive and accurate DNA methylation classifiers. After repeated cross-validation, a final classifier was derived that consisted of only 246 CpG sites. The mean sensitivity and specificity of the classifier across the known subtypes was 0.90 and 0.99, respectively. We then used DNA methylation classification to screen for subtype membership of 210 patients with undefined karyotype (normal or no result) or non-recurrent cytogenetic aberrations ('other' subtype). Nearly half (n = 106) of the patients lacking cytogenetic subgrouping displayed highly similar methylation profiles as the patients in the known recurrent groups. We verified the subtype of 20% of the newly classified patients by examination of diagnostic karyotypes, array-based copy number analysis, and detection of fusion genes by quantitative polymerase chain reaction (PCR) and RNA-sequencing (RNA-seq). Using RNA-seq data from ALL patients where cytogenetic subtype and DNA methylation classification did not agree, we discovered several novel fusion genes involving ETV6, RUNX1, and PAX5. CONCLUSIONS: Our findings indicate that DNA methylation profiling contributes to the clarification of the heterogeneity in cytogenetically undefined ALL patient groups and could be implemented as a complementary method for diagnosis of ALL. The results of our study provide clues to the origin and development of leukemic transformation. The methylation status of the CpG sites constituting the classifiers also highlight relevant biological characteristics in otherwise unclassified ALL patients.

The mutational landscape in pediatric acute lymphoblastic leukemia deciphered by whole genome sequencing.

Genomic characterization of pediatric acute lymphoblastic leukemia (ALL) has identified distinct patterns of genes and pathways altered in patients with well-defined genetic aberrations. To extend the spectrum of known somatic variants in ALL, we performed whole genome and transcriptome sequencing of three B-cell precursor patients, of which one carried the t(12;21)ETV6-RUNX1 translocation and two lacked a known primary genetic aberration, and one T-ALL patient. We found that each patient had a unique genome, with a combination of well-known and previously undetected genomic aberrations. By targeted sequencing in 168 patients, we identified KMT2D and KIF1B as novel putative driver genes. We also identified a putative regulatory non-coding variant that coincided with overexpression of the growth factor MDK. Our results contribute to an increased understanding of the biological mechanisms that lead to ALL and suggest that regulatory variants may be more important for cancer development than recognized to date. The heterogeneity of the genetic aberrations in ALL renders whole genome sequencing particularly well suited for analysis of somatic variants in both research and diagnostic applications.